AI bottlenecks are shifting to memory, optics, yield control and power
thedealdirector · x · 2026-07-21
AI bottlenecks are shifting from memory to networking, yield and power
The thread ranks today’s AI bottlenecks by importance and argues that memory and storage remain the first constraint because earnings revisions are happening now.
Key points:
- HBM remains structurally tight, but pricing strength is spreading to conventional DRAM, server memory and NAND.
- Connectivity and optics come next: 800G and 1.6T networks must scale with accelerator clusters, while copper is running into power and distance limits.
- Test, inspection and yield control matter because defects in HBM4 stacks or advanced packaging are too expensive to catch late.
- AI cloud capacity is a major bottleneck too: contracted demand is huge, but financing can consume equity before revenue shows up.
- Power and cooling remain critical constraints in the buildout.
Related event: DDR5 and Power Emerge as Biggest Bottlenecks in AI Supply Chain(2 posts)→
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